We evaluate the algorithm on three datasets, MultiWOZ 2.0 [1]}, CamRest676 [2]} and SMCalFlow [3]}, demonstrating that the noisy channel approach is robust to different dialogue schema annotations used across datasets. Further analysis demonstrates that the noisy channel models can decode responses with similar lengths and Zipf scores compared to ground-truth responses and reduce the likelihood of falling into repetition loops [4]}.
